In pathology, classification of tissue into normal type and tumor type can be challenging at times and requires detailed analysis of the fine structures of the cells and their morphology.
In traditional cancer diagnosis, (histo-)pathological images of biopsy samples are visually analyzed by pathologists. Next to this, also molecular methods (e.g., polymerase chain reaction (PCR) or sequencing) are used to identify biological characteristics of (tumor) tissue. Because the result of a molecular analysis heavily depends on the exact composition of the selected tissue region, a precise Region-of-Interest (ROI)-dissection is required. Currently, the ROI is often manually dissected using a scalpel, which is (highly) inaccurate. “Digital pathology” pursues the route of using image analysis algorithms that can be used to search for the ROI tissue. It has been found however that existing algorithms are, at times, slow or lack robustness.
WO 2010/125495 A2 disclosed that generate a first digital image of a first slice which comprises a region of interest, generate a second digital image of a second slice, and determine a region of interest in the second digital image on the basis of the region of interest in the first digital image, wherein the first digital image serves as the reference template.